A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market

Abstract

We analyze the statistical dependency structure of the S&P 500 constituents in the 4-year period from 2007 to 2010 using intraday data from the New York Stock Exchange’s TAQ database. With a copula-based approach, we ﬁnd that the statistical dependencies are very strong in the tails of the marginal distributions. This tail dependence is higher than in a bivariate Gaussian distribution,which is implied in the calculation of many correlation coeffcients. We compare the tail dependence to the market’s average correlation level as a commonly used quantity and disclose an neraly linear relation.